Description Usage Arguments Details Value Examples

`plotCalibration`

creates a plot showing the calibration of our calibration procedure

1 2 | ```
plotCalibration(logRr, seLogRr, useMcmc = FALSE, legendPosition = "right",
title, fileName = NULL)
``` |

`logRr` |
A numeric vector of effect estimates on the log scale |

`seLogRr` |
The standard error of the log of the effect estimates. Hint: often the standard error = (log(<lower bound 95 percent confidence interval>) - log(<effect estimate>))/qnorm(0.025) |

`useMcmc` |
Use MCMC to estimate the calibrated P-value? |

`legendPosition` |
Where should the legend be positioned? ("none", "left", "right", "bottom", "top") |

`title` |
Optional: the main title for the plot |

`fileName` |
Name of the file where the plot should be saved, for example 'plot.png'. See
the function |

Creates a calibration plot showing the number of effects with p < alpha for every level of alpha. The empirical calibration is performed using a leave-one-out design: The p-value of an effect is computed by fitting a null using all other negative controls. Ideally, the calibration line should approximate the diagonal. The plot shows both theoretical (traditional) and empirically calibrated p-values.

A Ggplot object. Use the `ggsave`

function to save to file.

1 2 3 | ```
data(sccs)
negatives <- sccs[sccs$groundTruth == 0, ]
plotCalibration(negatives$logRr, negatives$seLogRr)
``` |

OHDSI/EmpiricalCalibration documentation built on June 26, 2018, 7:12 a.m.

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